Diffusion - Revision history

BPeat at 11:44, 11 May 2024

2024-05-11T11:44:39Z

BPeat at 00:42, 27 April 2024

2024-04-27T00:42:08Z

BPeat at 01:15, 10 April 2024

2024-04-10T01:15:17Z

BPeat: Text replacement - "* Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu" to "* Conversational AI ... [[C...

2024-03-16T15:53:52Z

BPeat at 18:53, 16 September 2023

2023-09-16T18:53:17Z

BPeat at 03:04, 15 September 2023

2023-09-15T03:04:05Z

BPeat at 01:02, 3 September 2023

2023-09-03T01:02:59Z

BPeat at 00:41, 17 August 2023

2023-08-17T00:41:34Z

BPeat at 00:08, 13 July 2023

2023-07-13T00:08:44Z

BPeat at 17:18, 15 April 2023

2023-04-15T17:18:04Z

@@ Line 19: / Line 19: @@
 Diffusion Models are [[Generative AI|generative]] models, meaning that they are used to generate data similar to the data on which they are trained. Fundamentally, Diffusion Models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. [https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/#:~:text=Diffusion%20Models%20are%20generative%20models%2C%20meaning%20that%20they%20are%20used,by%20reversing%20this%20noising%20process. | Ryan O'Connor - AssmblyAI]
-−
 <youtube>yTAMrHVG1ew</youtube>
 <youtube>HoKDTa5jHvg</youtube>

@@ Line 15: / Line 15: @@
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Grok]] | [https://x.ai/ xAI] ... [[Groq]] ... [[Ernie]] | [[Baidu]]
 Diffusion Models are [[Generative AI|generative]] models, meaning that they are used to generate data similar to the data on which they are trained. Fundamentally, Diffusion Models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. [https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/#:~:text=Diffusion%20Models%20are%20generative%20models%2C%20meaning%20that%20they%20are%20used,by%20reversing%20this%20noising%20process. | Ryan O'Connor - AssmblyAI]

@@ Line 14: / Line 14: @@
 ** [https://www.assemblyai.com/blog/how-dall-e-2-actually-works/ How DALL-E 2 Actually Works | Ryan O'Connor - AssemblyAI]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Grok]] | [https://x.ai/ xAI] ... [[Groq]] ... [[Ernie]] | [[Baidu]]
 Diffusion Models are [[Generative AI|generative]] models, meaning that they are used to generate data similar to the data on which they are trained. Fundamentally, Diffusion Models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. [https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/#:~:text=Diffusion%20Models%20are%20generative%20models%2C%20meaning%20that%20they%20are%20used,by%20reversing%20this%20noising%20process. | Ryan O'Connor - AssmblyAI]

@@ Line 14: / Line 14: @@
 ** [https://www.assemblyai.com/blog/how-dall-e-2-actually-works/ How DALL-E 2 Actually Works | Ryan O'Connor - AssemblyAI]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 Diffusion Models are [[Generative AI|generative]] models, meaning that they are used to generate data similar to the data on which they are trained. Fundamentally, Diffusion Models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. [https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/#:~:text=Diffusion%20Models%20are%20generative%20models%2C%20meaning%20that%20they%20are%20used,by%20reversing%20this%20noising%20process. | Ryan O'Connor - AssmblyAI]

@@ Line 9: / Line 9: @@
 * [[Markov Model (Chain, Discrete Time, Continuous Time, Hidden)]]
-* [https://stablediffusionweb.com/ Stable Diffusion | Stable Diffusion] ... a latent text-to-image diffusion model capable of generating photo-realistic images given any text input
+* [https://stablediffusionweb.com/ Stable Diffusion | Stable Diffusion] ... a [[latent]] text-to-image diffusion model capable of generating photo-realistic images given any text input
 * [[Music#Stable Audio|Stable Audio]]
 * [[Video/Image#DALL-E | DALL-E]]

@@ Line 28: / Line 28: @@
 * [https://arxiv.org/abs/2302.03668 Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery | Y. Wen, N. Jain, J. Kirchenbauer, M. Goldblum, J. Geiping, and T. Goldstein]
-[https://github.com/lllyasviel/ControlNet ControlNet | llyasviel]:  We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications. [https://arxiv.org/abs/2302.05543 Adding Conditional Control to Text-to-Image Diffusion Models | Lvmin Zhang, Maneesh Agrawala]
+[https://github.com/lllyasviel/ControlNet ControlNet | llyasviel]:  We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as [[fine-tuning]] a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications. [https://arxiv.org/abs/2302.05543 Adding Conditional Control to Text-to-Image Diffusion Models | Lvmin Zhang, Maneesh Agrawala]
 There exists a very subtle but powerful methodology for deep learning. This simple idea is to use a neural network to extract the “specification” from an example. Then use another neural network to generate examples from the extracted example. This is a universal idea.  [https://medium.com/intuitionmachine/deep-learning-modularity-and-language-models-bd726c5e3b58 Deep Learning Modularity and Language Models | Carlos E. Perez - Medium]
 https://github.com/lllyasviel/ControlNet/raw/main/github_page/he.png